Abstract
Several studies have demonstrated the involvement of apolipoprotein C1 (APOC1) in multiple cancers. However, the role of APOC1 in esophageal cancer (ESCA) has not been elucidated. Hence, we examined the expression of APOC1 in ESCA tissues acquired from The Cancer Genome Atlas (TCGA) database and clinical samples from our hospital. An investigation of the association of APOC1 with the clinicopathological characteristics, prognosis, and diagnosis of ESCA was carried out on the basis of survival, receiver operating characteristics, and correlation analyses. Gene ontology, KEGG analysis, and protein-protein interaction network showed that co-expressed APOC1 genes were involved in the functions, mechanisms, and action network. The effects of APOC1 expression on ESCA cells were explored using CCK-8, migration and invasion assays. The relationship between APOC1 expression and ESCA immune-infiltrating cells and cell markers were examined using correlation analysis. We found that APOC1 was overexpressed in TCGA ESCA tissues and the same was validated in clinical ESCA tissues, with the area under the curve for APOC1 being 0.887. Overexpression of APOC1 was associated with short overall survival, disease-specific survival, progression-free interval, T stage, pathological stage, body mass index, and histological grade. Inhibition of APOC1 expression significantly reduced the proliferation, migration, and invasion of ESCA cells. Furthermore, APOC1 expression positively correlated with the ESTIMATE, immune, and stromal scores in ESCA. Overexpression of APOC1 correlated with the tumor purity, B cells, T helper cells, natural killer cells, cytotoxic cells, and other immune cells. Moreover, APOC1 was involved in ESCA progression via T cell receptor, B cell receptor, and other immune signaling pathways. Thus, APOC1 overexpression is expected to be a biomarker for dismal prognosis and diagnosis of ESCA. Inhibition of APOC1 expression significantly reduced the proliferation, migration, and invasion of ESCA cells. Overexpression of APOC1 was associated with the immune microenvironment in ESCA. Thus, APOC1 may be an efficient biomarker for proper prognosis and diagnosis of ESCA.
Keywords: APOC1, ESCA, immune microenvironment, prognosis, biomarker
Introduction
Esophageal cancer (ESCA) is one of the most common malignant tumors worldwide [1,2]. At present, the incidence of ESCA is low in Western countries but high in China. There is a lack of effective techniques for early diagnosis and treatment of ESCA patients. Several patients with ESCA visit the hospital due to eating obstructions. However, by then, the tumor has already reached the middle and advanced stages, therefore, the survival of such patients remains dismal even after surgical treatment. Hence, novel diagnostic techniques and therapies are needed to improve the prognosis and treatment of ESCA patients. Currently, targeted therapy in cancer has shown a great therapeutic value and is expected to improve the prognosis of cancer patients [3-6]. For example, the overall survival (OS) was significantly longer in patients with non-small cell lung cancer (NSCLC) treated with erlotinib than when treated with a placebo [3]. The PD-1/PD-L1 inhibitor improved OS and progression-free survival in North American and European cancer patients. Further, compared to European cancer patients, North American patients benefit more from the PD-1/PD-L1 inhibitors [4]. These results suggest that targeted therapy can potentially improve the prognosis of cancer patients.
Several studies have shown that elevated or inhibited gene expression can delay the progression of ESCA [7-9]. For example, importin 5 (IPO5) expression is significantly higher in tissues of patients with ESCA than in adjacent esophageal tissues. Increased IPO5 expression is associated with late pathological stage and short OS in ESCA patients. Interfering with IPO5 expression decreases ESCA cell proliferation and promotes MMP7 protein expression in ESCA cells. Thus, IPO5 might promote the malignant progression of ESCA by regulating MMP7 expression [7]. SIX homeobox 4 (SIX4) is significantly upregulated in esophageal squamous cell carcinoma (ESCC) tissues and associated with adverse clinical outcomes in ESCC patients. SIX4 knockdown inhibits the proliferation, migration and invasion of ESCC cells, enhances their ability to induce ESCC cell apoptosis, and inhibits epithelial to mesenchymal transition (EMT). Upregulation of SIX4 activates the PI3K/AKT signaling pathway in ESCC cells and promotes tumor growth in vivo [9]. Such proteins can act as novel prognostic biomarkers to track ESCA progression and predict the prognosis of patients with ESCA.
In recent years, apolipoprotein C1 (APOC1) has been associated with cancer progression [10-14]. In colorectal cancer (CRC) tissues, APOC1 is significantly overexpressed, and its overexpression is associated with lymph node metastasis, tumor-node-metastasis (TNM) stage, distant metastasis, and poor prognosis in CRC patients. APOC1 overexpression affects the growth and migration of CRC cells through the MAPK signaling pathway [10]. Further, it is an independent risk factor for the OS in CRC patients [10]. In addition, APOC1 concentration in the serum and tissues of patients with gastric cancer is significantly higher than that in the control group. Elevated APOC1 expression significantly correlates with the clinical stage, tumor classification, lymph node metastasis, and poor prognosis in gastric cancer patients and thus has diagnostic value in gastric cancer [11]. Moreover, APOC1 expression is significantly upregulated in renal cell carcinoma (ccRCC). High APOC1 levels are associated with poor survival in ccRCC patients, and APOC1 enhances ccRCC metastasis by promoting STAT3 activation [14]. However, to date, the roles and mechanisms of APOC1 in the development of ESCA have not been reported. Therefore, this study aimed to identify APOC1 expression in ESCA tissues using The Cancer Genome Atlas (TCGA) database and clinical tissues. We also explored the roles and potential mechanisms of APOC1 in ESCA by receiver operating characteristic (ROC) analysis, survival and correlation analysis, gene set enrichment analysis (GSEA), and cell experiments.
Materials and methods
Clinical ESCA tissues
From 2016 to 2021, cancer and adjacent tissues were acquired from 40 ESCA patients in the pathology department of our hospital. The clinical data of ESCA patients are detailed in Table 1. Our study was reviewed and approved by the ethics committee of Taihe Hospital. Immunohistochemistry revealed the expression of APOC1 in ESCA tissues. For immunohistochemistry, we performed deparaffinization, antigen retrieval, and blocking according to routine methods, and the primary antibody concentration was 1:100 [15]. The APOC1 protein expression was calculated for normal and ESCA tissues. The results were analyzed using the staining index, which is a combination of the percentage of positive cells (staining range) and the intensity of staining. The staining index was calculated as the staining range multiplied by the staining intensity. The experiments and analyses were performed under double-blind conditions.
Table 1.
The clinical information of ESCA patients
| Baseline | N | High APOC1 expression | Low APOC1 expression | Percentage (%) |
|---|---|---|---|---|
| Gender | 40 | |||
| Male | 31 | 27 | 4 | 77.5 |
| Female | 9 | 7 | 2 | 22.5 |
| Age | 40 | |||
| ≤ 60 | 25 | 21 | 4 | 62.5 |
| > 60 | 15 | 13 | 2 | 37.5 |
| Tumor size | 40 | |||
| T1-2 | 17 | 12 | 5 | 42.5 |
| T3-4 | 23 | 22 | 1 | 57.5 |
| Lymph node metastasis | 40 | |||
| No | 25 | 20 | 5 | 62.5 |
| Yes | 15 | 14 | 1 | 37.5 |
| Tumor grade | 40 | |||
| G1-2 | 30 | 27 | 3 | 75 |
| G3 | 10 | 7 | 3 | 25 |
Gene expression of ESCA patient from TCGA database
In August 2021, gene expression data of fragments per kilo base of exon per million mapped (FPKM) types in pan-cancer and normal tissues, along with the prognostic data and clinicopathological characteristics of ESCA patients were downloaded from the TCGA database. Gene expression data from pan-cancer tissues were sorted and merged, and the expression of APOC1 in pan-cancer tissues, paired pan-cancer tissues, and normal tissues were calculated using the t-test.
Exploring the relationship between APOC1 expression and the clinicopathological features of ESCA
APOC1 gene expression data and clinicopathological characteristics of ESCA patients were merged. Patients with ESCA were grouped according to the clinicopathological features and the median expression value of APOC1. This grouping explored the relationship between the APOC1 expression and clinicopathological features of ESCA patients.
Diagnostic and prognostic values of APOC1 in ESCA
ROC analysis is often used to evaluate the diagnostic value of genes in cancer. If the area under the ROC curve is close to 1, the diagnostic value is high [15-17]. The diagnostic value of APOC1 in ESCA in normal and cancerous tissues were analyzed using ROC. The relationship between APOC1 expression and OS, disease-specific survival, and progression-free interval was explored in ESCA patients using the Kaplan-Meier survival analysis according to the best cut-off value.
Functions and mechanisms of APOC1 co-expressed genes
We performed a correlation analysis to explore the genes co-expressed with APOC1 in the tissues of ESCA patients obtained from the TCGA database. If the correlation coefficient was close to 1, the two genes were more related [18]. We considered a value of P < 0.001, and r > 0.5 or r < -0.5 of the correlation coefficients as significantly correlated. The functions and signaling mechanisms of the APOC1 co-expressed genes were also explored using gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis.
Protein-protein interaction (PPI) network
The PPI network between APOC1 co-expressed genes was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. In the PPI network, a binding score > 0.4 was used as the screening criterion, and unconnected genes were removed. The PPI network was then imported into the Cytoscape software for network visualization. Enrichment analysis of APOC1 co-expressed genes in the PPI network was performed using the MCODE plugin [19,20].
Gene set enrichment analysis (GSEA) analysis
Gene expression data of ESCA patients in TCGA database were grouped by the median value of APOC1 expression and were further divided into APOC1 high- and low-phenotype groups. In order to understand the underlying signaling mechanisms by which APOC1 might affect ESCA progression, the effects of APOC1 high- and low-phenotype groups on TCGA gene set were explored using KEGG analysis in GSEA (version: 4.1.0) software [21]. This cycle was performed 1000 times, and nominal (NOM) P < 0.05 was the screening criterion for potential signaling mechanisms.
Construction of APOC1 cell model
The ESCA cells were fed with 10% fetal bovine serum (FBS) complete DMEM media. An appropriate amount of ESCA cells was seeded in six-well plates. Transfection was performed according to the manufacturer’s instructions of GeneCopoeia (Guangzhou, China) for siRNA. The interfering sequences of APOC1 were 5’-GCAUCAAACAGAGUGAACUTT-3’ (siRNA-1), and 5’-GCCGCAUCAAACAGAGU GATT-3’ (siRNA-2), and the expression levels of APOC1 in ESCA cell models were tested by the standard PCR procedure [21].
Cell counting kit (CCK-8) assay
Cells from the control group (NC) and APOC1 inhibition group (si-APOC1) were plated on the 96-well plates, and 10 µl of the CCK-8 solution was added after cells adhered, and the time was recorded as 0 h. In addition, 10 µl of the CCK-8 solution was added at 24, 48, and 72 h of cell incubation. Furthermore, the cells were incubated for 2 h and the absorbance was detected.
Cell migration and invasion
Serum-containing complete medium was added to the lower chamber and serum-free cell suspension was added to the upper chamber. Both the chambers were incubated for 24 h. Post incubation, liquid from the upper chamber was discarded. Upper chamber cells on the membrane that had not passed through the membrane were wiped with a wet cotton swab. Crystal violet staining was performed, and after rinsing, slides were mounted, observed, and the cells were counted.
Analysis of the relationship between APOC1 expression and immune cell infiltration
The relationship between APOC1 expression and ESCA immune cell levels was verified using the Tumor Immune Estimation Resource (TIMER) (https://cistrome.shinyapps.io/timer/) database [22,23]. The ESTIMATE, immune and stromal scores, and immune cell levels in the tissues of ESCA patients from TCGA database were calculated using the ESTIMATE and Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) analysis methods. In the ESCA tissues, the association of APOC1 expression with the ESTIMATE, immune and stromal scores, and immune cell levels was explored using Pearson correlation analysis. In addition, the median value of APOC1 expression was divided into high- and low-expression groups. Further, the ESTIMATE, immune and stromal scores, and the immune cell levels in APOC1 high- and low-expression groups were investigated.
Analysis and identification of the relationship between APOC1 and immune cell markers
The relationship between APOC1 expression and ESCA immune infiltrating cell markers was examined using the correlation analysis module of the TIMER database. Under the conditions of tumor purity and non-purity, the relationship between APOC1 and immune cell marker levels was investigated, with P < 0.05 as the filter criterion. The relationship between significant cellular markers and APOC1 expression in the TIMER database was identified using the GEPIA database.
Statistical analysis
The expression of APOC1 in pan-cancerous tissues was explored using the t-test. The role of APOC1 in the diagnosis and prognosis of ESCA was analyzed by ROC and survival analysis. Co-expressed genes of APOC1 were screened using the correlation analysis to determine the relationship between APOC1 and the clinicopathological features and immune infiltrating cells of ESCA. Statistical significance was set at P < 0.05.
Results
APOC1 was significantly overexpressed in ESCA tissues
Compared to the unpaired normal tissues from the TCGA database, APOC1 was significantly overexpressed in bladder urothelial carcinoma, colon adenocarcinoma, ESCA, breast invasive carcinoma, glioblastoma multiforme, head and neck squamous cell carcinoma, endocervical adenocarcinoma, kidney chromophobe, kidney renal papillary cell carcinoma, stomach adenocarcinoma, uterine corpus endometrial carcinoma, kidney renal clear cell carcinoma, and thyroid carcinoma tissues. Conversely, APOC1 expression significantly decreased in lung adenocarcinoma, cholangiocarcinoma, and lung squamous cell carcinoma tissues (Figure 1A). Compared to paired normal tissues, APOC1 was significantly overexpressed in the paired bladder urothelial carcinoma, colon adenocarcinoma, kidney renal clear cell carcinoma, kidney renal papillary cell carcinoma, ESCA, head and neck squamous cell carcinoma, kidney chromophobe, prostate adenocarcinoma, stomach adenocarcinoma, thyroid carcinoma, breast invasive carcinoma, and uterine corpus endometrial carcinoma tissues; whereas APOC1 expression significantly decreased in the cholangiocarcinoma, hepatocellular carcinoma, lung adenocarcinoma, and lung squamous cell carcinoma tissues (Figure 1B). Among the 40 ESCA samples obtained from our hospital, APOC1 protein expression was significantly elevated in 34 (85%) patients, which was considered statistically significant (Figure 2).
Figure 1.
MRNA expression of APOC1 in pan-cancer tissues. A. Unpaired cancer tissues; B. Paired cancer tissues. *, P < 0.05; **, P < 0.01; ***, P < 0.001; ns, not statistically significant.
Figure 2.
Protein expression of APOC1 in paired EC tissues. EC, Esophageal Cancer; ESCA, Esophageal Cancer.
APOC1 overexpression was associated with the diagnosis and dismal prognosis of ESCA
ROC analysis showed that the Area Under the ROC curve (AUC) of APOC1 was 0.887, which was the diagnostic indicative of ESCA (Figure 3A). Kaplan-Meier survival analysis showed a poor prognosis in ESCA patients with APOC1 overexpression (Figure 3B-D). APOC1 overexpression was significantly associated with a short OS, disease-specific survival, and progression-free interval in ESCA patients.
Figure 3.
Clinical diagnostic and prognostic roles of APOC1 in ESCA. A. Diagnosis; B. OS; C. DSS; D. PFI. OS, Overall Survival; DSS, Disease-Specific Survival; PFI, Progress-Free Interval; ESCA, Esophageal Cancer.
APOC1 overexpression was associated with the T stage, pathological stage, body mass index (BMI), and histological grade in ESCA patients
In ESCA tissues obtained from the TCGA database, we found that APOC1 expression was correlated with the T stage (which refers to the size and extent of the main tumor), pathological stage, BMI, and histological grade of ESCA patients grouped by the median expression of APOC1 (Table 2). Abnormal APOC1 expression in terms of BMI, T stage, and pathological stage was revealed by grouping according to clinicopathological features of ESCA (Figure 4). Logistic regression analysis showed that the expression of APOC1 was abnormal in the T stage (T3-4 versus T1-2), pathological stage (Stage III-IV versus Stage I-II), and BMI (> 25 versus ≤ 25) (Table 3).
Table 2.
Association of APOC1 overexpression with the clinicopathological features in ESCA patients
| Characteristic | Low APOC1 expression | High APOC1 expression | P |
|---|---|---|---|
| T stage | 0.012 | ||
| T1 | 18 (12.4%) | 9 (6.2%) | |
| T2 | 21 (14.5%) | 16 (11%) | |
| T3 | 31 (21.4%) | 46 (31.7%) | |
| T4 | 0 (0%) | 4 (2.8%) | |
| N stage | 0.424 | ||
| N0 | 37 (25.7%) | 29 (20.1%) | |
| N1 | 29 (20.1%) | 34 (23.6%) | |
| N2 | 3 (2.1%) | 6 (4.2%) | |
| N3 | 2 (1.4%) | 4 (2.8%) | |
| M stage | 1.000 | ||
| M0 | 57 (44.2%) | 64 (49.6%) | |
| M1 | 4 (3.1%) | 4 (3.1%) | |
| Pathologic stage | 0.005 | ||
| Stage I | 13 (9.2%) | 3 (2.1%) | |
| Stage II | 36 (25.4%) | 33 (23.2%) | |
| Stage III | 16 (11.3%) | 33 (23.2%) | |
| Stage IV | 4 (2.8%) | 4 (2.8%) | |
| Radiation therapy | 0.703 | ||
| No | 55 (38.2%) | 52 (36.1%) | |
| Yes | 17 (11.8%) | 20 (13.9%) | |
| Primary therapy outcome | 0.175 | ||
| PD | 7 (7.4%) | 3 (3.2%) | |
| SD | 3 (3.2%) | 4 (4.3%) | |
| PR | 0 (0%) | 3 (3.2%) | |
| CR | 42 (44.7%) | 32 (34%) | |
| Gender | 0.177 | ||
| Female | 15 (9.3%) | 8 (4.9%) | |
| Male | 66 (40.7%) | 73 (45.1%) | |
| Race | 0.787 | ||
| Asian | 18 (12.5%) | 20 (13.9%) | |
| Black or African American | 4 (2.8%) | 2 (1.4%) | |
| White | 49 (34%) | 51 (35.4%) | |
| Age | 0.346 | ||
| ≤ 60 | 45 (27.8%) | 38 (23.5%) | |
| > 60 | 36 (22.2%) | 43 (26.5%) | |
| Weight | 1.000 | ||
| ≤ 70 | 38 (23.8%) | 38 (23.8%) | |
| > 70 | 43 (26.9%) | 41 (25.6%) | |
| Height | 1.000 | ||
| < 170 | 24 (15.7%) | 23 (15%) | |
| ≥ 170 | 54 (35.3%) | 52 (34%) | |
| BMI | 0.040 | ||
| ≤ 25 | 36 (23.5%) | 48 (31.4%) | |
| > 25 | 42 (27.5%) | 27 (17.6%) | |
| Histological type | 0.157 | ||
| Adenocarcinoma | 35 (21.6%) | 45 (27.8%) | |
| Squamous Cell Carcinoma | 46 (28.4%) | 36 (22.2%) | |
| Residual tumor | 0.764 | ||
| R0 | 62 (46.3%) | 59 (44%) | |
| R1 | 4 (3%) | 7 (5.2%) | |
| R2 | 1 (0.7%) | 1 (0.7%) | |
| Histologic grade | 0.027 | ||
| G1 | 11 (8.7%) | 5 (4%) | |
| G2 | 36 (28.6%) | 30 (23.8%) | |
| G3 | 15 (11.9%) | 29 (23%) | |
| Smoker | 0.552 | ||
| No | 21 (14.6%) | 26 (18.1%) | |
| Yes | 50 (34.7%) | 47 (32.6%) | |
| Alcohol history | 0.635 | ||
| No | 21 (13.2%) | 25 (15.7%) | |
| Yes | 58 (36.5%) | 55 (34.6%) | |
| Barretts esophagus | 0.511 | ||
| No | 51 (38.6%) | 55 (41.7%) | |
| Yes | 15 (11.4%) | 11 (8.3%) | |
| Reflux history | 0.967 | ||
| No | 42 (30.9%) | 42 (30.9%) | |
| Yes | 25 (18.4%) | 27 (19.9%) | |
| Tumor cental location | 0.168 | ||
| Distal | 55 (34.2%) | 58 (36%) | |
| Mid | 24 (14.9%) | 18 (11.2%) | |
| Proximal | 1 (0.6%) | 5 (3.1%) | |
| Columnar mucosa dysplasia | 0.363 | ||
| High grade dysplasia | 14 (20.6%) | 11 (16.2%) | |
| Low grade dysplasia | 1 (1.5%) | 4 (5.9%) | |
| Negative/no dysplasia | 21 (30.9%) | 17 (25%) | |
| Columnar metaplasia | 0.543 | ||
| No | 39 (39.8%) | 31 (31.6%) | |
| Yes | 13 (13.3%) | 15 (15.3%) | |
| OS event | 0.200 | ||
| Alive | 53 (32.7%) | 44 (27.2%) | |
| Dead | 28 (17.3%) | 37 (22.8%) | |
| DSS event | 0.105 | ||
| Alive | 63 (39.1%) | 52 (32.3%) | |
| Dead | 18 (11.2%) | 28 (17.4%) | |
| PFI event | 0.209 | ||
| Alive | 46 (28.4%) | 37 (22.8%) | |
| Dead | 35 (21.6%) | 44 (27.2%) |
ESCA, Esophageal Cancer.
Figure 4.
Association of APOC1 overexpression with the BMI, pathological stage, and T stage in ESCA patients. A. BMI; B. Stage I vs II; C. Stage I vs III; D. T1 vs T4; E. T2 vs T4; F. T3 vs T4. ESCA, Esophageal Cancer; BMI, Body Mass Index; *, P < 0.05; **, P < 0.01; ***, P < 0.001.
Table 3.
Association of APOC1 overexpression associated with the clinicopathological features in ESCA patients using logistics regression analysis
| Characteristics | Total (N) | OR | P |
|---|---|---|---|
| T stage (T3-T4 vs T1-2) | 145 | 2.516 (1.293-4.983) | 0.007 |
| N stage (N1-3 vs N0) | 144 | 1.651 (0.855-3.217) | 0.137 |
| M stage (M1 vs M0) | 129 | 0.891 (0.202-3.923) | 0.874 |
| Pathologic stage (Stage III-IV vs Stage I-II) | 142 | 2.518 (1.270-5.105) | 0.009 |
| Radiation therapy (Yes vs No) | 144 | 1.244 (0.588-2.655) | 0.568 |
| Gender (Male vs Female) | 162 | 2.074 (0.844-5.445) | 0.120 |
| Age (> 60 vs ≤ 60) | 162 | 1.414 (0.763-2.636) | 0.272 |
| BMI (> 25 vs ≤ 25) | 153 | 0.482 (0.250-0.917) | 0.027 |
| Weight (> 70 vs ≤ 70) | 160 | 0.953 (0.512-1.775) | 0.880 |
| Height (≥ 170 vs < 170) | 153 | 1.005 (0.505-2.004) | 0.989 |
| Smoker (Yes vs No) | 144 | 0.759 (0.375-1.525) | 0.440 |
| Histologic grade (G2-3 vs G1) | 126 | 2.545 (0.864-8.527) | 0.103 |
| Histological type (SCC vs Adenocarcinoma) | 162 | 0.609 (0.325-1.129) | 0.117 |
SCC, Squamous Cell Carcinoma; OR, Odds Ratio.
Functional mechanisms and protein action network of APOC1 co-expressed genes
There were 229 APOC1 co-expressed genes that positively correlated with APOC1 (Figure 5 and Table 4). The top 20 APOC1 positively correlated genes by fold-change were visualized using a heatmap (Figure S1). KEGG analysis showed that APOC1 co-expressed genes were involved in cell adhesion, Th1, Th2, and Th17 cell differentiation, antigen processing and presentation, natural killer (NK) cell-mediated cytotoxicity, T cell receptor signaling pathway, chemokine signaling pathway, primary immunodeficiency, cytokine-cytokine receptor interaction, B cell receptor signaling pathway, PD-L1 expression and PD-1 checkpoint pathway in cancer, Toll-like receptor signaling pathway; leukocyte trans endothelial migration, and others (Table 5). Gene ontology annotation analysis showed that APOC1 co-expressed genes were involved in the regulation of leukocyte, lymphocyte, and T-cell activation, positive regulation of cell and leukocyte activation, regulation of lymphocyte and mononuclear cell proliferation, leukocyte cell-cell adhesion, immune response-activating cell surface receptor signaling pathway, leukocyte differentiation, T-cell proliferation, and other functions (Table S1). Figure 6A shows the PPI network between genes positively related to APOC1 and the results of enrichment analysis of genes positively associated with APOC1 using the MCODE plugin (Figure 6B-D).
Figure 5.
APOC1 co-expressed genes in ESCA tissues. A. APOE; B. TYROBP; C. FCGR1A; D. HAVCR2; E. LAIR1; F. FCER1G; G. C1QB; H. C1QA; I. MS4A4A. ESCA, Esophageal Cancer.
Table 4.
APOC1 co-expressed genes in ESCA tissues
| Gene | cor | Gene | cor | Gene | cor | Gene |
|---|---|---|---|---|---|---|
| LY86 | 0.618 | C3AR1 | 0.718 | LAIR1 | 0.777 | GAB3 |
| TMIGD3 | 0.687 | CXCL9 | 0.519 | FOLR2 | 0.563 | HLA-DRA |
| SLA | 0.604 | LAT2 | 0.517 | LPL | 0.513 | ARHGAP9 |
| ACP5 | 0.59 | LILRB2 | 0.587 | CCL5 | 0.504 | VSIG4 |
| VAV1 | 0.501 | FGR | 0.535 | CD163 | 0.63 | CD74 |
| FMNL1 | 0.571 | SLAMF8 | 0.737 | GGT5 | 0.515 | CD96 |
| MNDA | 0.681 | LCP2 | 0.64 | TREM2 | 0.649 | SPN |
| C1QA | 0.763 | TFEC | 0.664 | FPR3 | 0.723 | SIGLEC1 |
| GIMAP7 | 0.518 | CD86 | 0.667 | CYTIP | 0.537 | CD72 |
| RNASE6 | 0.696 | ABI3 | 0.648 | CMKLR1 | 0.632 | APBB1IP |
| C1orf54 | 0.693 | CCL4 | 0.575 | LRRC25 | 0.724 | ADA2 |
| LST1 | 0.686 | OSCAR | 0.641 | PDCD1 | 0.526 | WAS |
| PLD3 | 0.505 | MILR1 | 0.584 | CD53 | 0.705 | HCLS1 |
| HK3 | 0.685 | CD52 | 0.674 | LAPTM5 | 0.746 | HLA-DPB1 |
| SPI1 | 0.754 | EVI2B | 0.626 | IL2RB | 0.516 | ITGAX |
| GPR183 | 0.569 | MPP1 | 0.527 | SH2D1A | 0.539 | NCF4 |
| BTK | 0.614 | EVI2A | 0.699 | PLEKHO2 | 0.543 | HCK |
| SAMSN1 | 0.623 | ITGAM | 0.533 | HLA-DQA2 | 0.512 | APOE |
| PYHIN1 | 0.567 | APOC1 | 1 | ARHGAP25 | 0.55 | MS4A6A |
| DOK2 | 0.696 | DOCK2 | 0.586 | CXCR6 | 0.533 | GZMH |
| RGS18 | 0.606 | HAVCR2 | 0.781 | C5AR1 | 0.605 | SERPING1 |
| FABP3 | 0.532 | LY96 | 0.593 | GPSM3 | 0.604 | ICOS |
| LYL1 | 0.526 | CD300A | 0.659 | NFAM1 | 0.585 | CSF1R |
| STAT4 | 0.589 | RASSF4 | 0.541 | NCKAP1L | 0.649 | NRROS |
| TNFSF12 | 0.507 | ZEB2 | 0.512 | NR1H3 | 0.539 | TMEM273 |
| SIGLEC9 | 0.683 | LILRB3 | 0.581 | CCR1 | 0.665 | MRC1 |
| CSF2RA | 0.508 | CORO1A | 0.513 | P2RY13 | 0.502 | CD2 |
| SASH3 | 0.555 | MYO1F | 0.654 | C1orf162 | 0.723 | MMP19 |
| EBI3 | 0.568 | THEMIS2 | 0.559 | GPR34 | 0.614 | CD48 |
| GZMK | 0.514 | ARHGEF6 | 0.501 | DAB2 | 0.55 | LIPA |
| FOXP3 | 0.554 | CYTH4 | 0.611 | FLI1 | 0.504 | P2RX7 |
| TNFAIP8L2 | 0.689 | BIN2 | 0.601 | GPR171 | 0.535 | ARHGAP45 |
| IL2RA | 0.518 | PTPRC | 0.572 | WIPF1 | 0.534 | LCP1 |
| TNFSF13B | 0.621 | CLEC4A | 0.571 | CXorf21 | 0.599 | SDS |
| IFI30 | 0.657 | RENBP | 0.553 | C1QC | 0.751 | HLA-DMB |
| IL12RB1 | 0.507 | CD3E | 0.561 | GIMAP4 | 0.66 | PSTPIP1 |
| SEPT6 | 0.515 | CD4 | 0.725 | HLA-DPA1 | 0.521 | RASAL3 |
| LSP1 | 0.509 | PLEK | 0.538 | CCL18 | 0.53 | OLFML3 |
| CD3G | 0.526 | HLA-DQA1 | 0.591 | PLXNC1 | 0.574 | CCR5 |
| FCER1G | 0.772 | CD8B | 0.536 | MS4A7 | 0.742 | CTSW |
| GPR65 | 0.592 | LILRB4 | 0.735 | CTSL | 0.547 | CYBB |
| KLHL6 | 0.502 | IFFO1 | 0.526 | SNX20 | 0.533 | HSD17B14 |
| CD247 | 0.524 | SELPLG | 0.573 | VAMP5 | 0.552 | CD300C |
| ITGB2 | 0.725 | PILRA | 0.632 | CTLA4 | 0.516 | CALHM6 |
| C1QB | 0.766 | IGSF6 | 0.729 | TNFRSF4 | 0.567 | CD3D |
| PIK3R6 | 0.594 | SRGN | 0.629 | SCIMP | 0.574 | IL4I1 |
| PIK3R5 | 0.579 | TIGIT | 0.582 | LPXN | 0.533 | MSR1 |
| RGS1 | 0.576 | SIGLEC10 | 0.633 | FCGR3A | 0.76 | CD37 |
| LILRB1 | 0.627 | SEPT4 | 0.521 | IGFLR1 | 0.505 | FCGR2A |
| SIT1 | 0.511 | FCGR1A | 0.792 | PLEKHO1 | 0.516 | ITGAL |
| SLA2 | 0.521 | OLR1 | 0.596 | PLCB2 | 0.591 | ARHGDIB |
| GLIPR2 | 0.561 | CD8A | 0.54 | ADAMDEC1 | 0.66 | CD84 |
| AIF1 | 0.747 | DOCK10 | 0.519 | CD14 | 0.636 | TYROBP |
| CD300LF | 0.706 | DOK3 | 0.556 | SIRPG | 0.553 | GMFG |
| NKG7 | 0.596 | MPEG1 | 0.568 | GNGT2 | 0.659 | GIMAP1 |
| PARVG | 0.69 | SLC15A3 | 0.538 | IL10RA | 0.624 | MS4A4A |
| HCST | 0.711 | APOBR | 0.539 | PLA2G7 | 0.719 | LAG3 |
| GIMAP6 | 0.502 |
Table 5.
Signaling pathways of APOC1 co-expressed genes using KEGG analysis
| ID | Description | FDR |
|---|---|---|
| hsa04640 | Hematopoietic cell lineage | 1.48E-14 |
| hsa05150 | Staphylococcus aureus infection | 8.46E-14 |
| hsa04514 | Cell adhesion molecules (CAMs) | 1.27E-13 |
| hsa04145 | Phagosome | 2.50E-10 |
| hsa04658 | Th1 and Th2 cell differentiation | 8.16E-10 |
| hsa04380 | Osteoclast differentiation | 8.84E-10 |
| hsa05152 | Tuberculosis | 2.63E-09 |
| hsa04659 | Th17 cell differentiation | 4.75E-09 |
| hsa05140 | Leishmaniasis | 5.60E-09 |
| hsa05323 | Rheumatoid arthritis | 5.94E-09 |
| hsa04612 | Antigen processing and presentation | 8.14E-08 |
| hsa04660 | T cell receptor signaling pathway | 2.26E-07 |
| hsa04062 | Chemokine signaling pathway | 1.16E-06 |
| hsa04672 | Intestinal immune network for IgA production | 1.16E-06 |
| hsa05340 | Primary immunodeficiency | 1.89E-06 |
| hsa05322 | Systemic lupus erythematosus | 3.27E-06 |
| hsa05416 | Viral myocarditis | 5.80E-06 |
| hsa05310 | Asthma | 5.80E-06 |
| hsa04610 | Complement and coagulation cascades | 6.24E-06 |
| hsa05321 | Inflammatory bowel disease (IBD) | 1.01E-05 |
| hsa05320 | Autoimmune thyroid disease | 1.93E-05 |
| hsa05145 | Toxoplasmosis | 1.93E-05 |
| hsa05330 | Allograft rejection | 1.98E-05 |
| hsa05166 | Human T-cell leukemia virus 1 infection | 2.55E-05 |
| hsa05332 | Graft-versus-host disease | 3.11E-05 |
| hsa04061 | Viral protein interaction with cytokine and cytokine receptor | 4.01E-05 |
| hsa04940 | Type I diabetes mellitus | 4.01E-05 |
| hsa04060 | Cytokine-cytokine receptor interaction | 4.72E-05 |
| hsa04650 | Natural killer cell mediated cytotoxicity | 6.76E-05 |
| hsa05133 | Pertussis | 0.000213265 |
| hsa05142 | Chagas disease | 0.000276609 |
| hsa05169 | Epstein-Barr virus infection | 0.00072356 |
| hsa04666 | Fc gamma R-mediated phagocytosis | 0.000824484 |
| hsa04662 | B cell receptor signaling pathway | 0.00218021 |
| hsa04611 | Platelet activation | 0.005440719 |
| hsa05020 | Prion diseases | 0.012736279 |
| hsa05235 | PD-L1 expression and PD-1 checkpoint pathway in cancer | 0.017381189 |
| hsa05202 | Transcriptional misregulation in cancer | 0.018176628 |
| hsa05221 | Acute myeloid leukemia | 0.023013489 |
| hsa04620 | Toll-like receptor signaling pathway | 0.034245002 |
| hsa04979 | Cholesterol metabolism | 0.040539463 |
| hsa04670 | Leukocyte transendothelial migration | 0.046144849 |
Figure 6.
PPI network of APOC1 co-expressed genes. A. PPI network; B-D. Enriched network using MCODE method. PPI, Protein-Protein Interaction.
Downregulation of APOC1 expression reduced cell growth and metastasis of ESCA
Figure 7A and 7B show a successful construction of cell models intended to suppress APOC1 expression. Inhibition of APOC1 expression reduced the proliferation of ESCA EC109 and TE-1 cells as analyzed by CCK-8 (Figure 7C). Compared with EC109 and TE-1 cells in the control groups, migration and invasion ability of EC109 and TE-1 cells in the downregulation of APOC1 expression groups decreased significantly, and had significant statistical significance (Figures 7D-G and 8A-D). Preliminary results showed that APOC1 could play a biological role as a carcinogen gene, and down-regulation of APOC1 expression could delay cancer cell progression in ESCA.
Figure 7.
Downregulation of APOC1 expression inhibited ESCA cell proliferation, and invasion. A, B. Cell model; C. Cell proliferation using CCK-8; D-G. Cell invasion using Transwell. ESCA, Esophageal Cancer; **, P < 0.01; ***, P < 0.001.
Figure 8.
Downregulation of APOC1 expression inhibited ESCA cell migration. ESCA, Esophageal Cancer; ***, P < 0.001.
APOC1 was involved in the signaling mechanisms of ESCA progression
Grouped by the median value of APOC1 expression, cell adhesion molecules, hematopoietic cell lineage, antigen processing and presentation, NK cell-mediated cytotoxicity, graft versus host disease, cytokine-cytokine receptor interaction, primary immunodeficiency, ABC transporters, extracellular membrane receptor interaction, and several signaling pathways, including the T cell receptor, chemokine, B cell receptor, Toll-like receptor, JAK-STAT, and NOTCH pathways, were significantly enriched in the APOC1 overexpression group by GSEA analysis (Figure S2 and Table 6).
Table 6.
Signaling mechanisms involved in APOC1 overexpression using GSEA analysis
| Name | Size | NES | NOM p |
|---|---|---|---|
| Cell adhesion molecules cams | 131 | 2.2515292 | 0 |
| Hematopoietic cell lineage | 85 | 2.2381618 | 0 |
| Leishmania infection | 69 | 2.2281723 | 0 |
| Antigen processing and presentation | 80 | 2.2142258 | 0 |
| Systemic lupus erythematosus | 54 | 2.1937685 | 0 |
| Viral myocarditis | 68 | 2.1700046 | 0 |
| Lysosome | 121 | 2.165117 | 0 |
| Natural killer cell mediated cytotoxicity | 131 | 2.1341667 | 0 |
| Intestinal immune network for iga production | 46 | 2.1176693 | 0 |
| Graft versus host disease | 37 | 2.0940528 | 0 |
| Type i diabetes mellitus | 41 | 2.0691876 | 0 |
| Cytokine-cytokine receptor interaction | 263 | 2.0405216 | 0 |
| Leukocyte transendothelial migration | 116 | 2.0341792 | 0 |
| T cell receptor signaling pathway | 108 | 2.0321288 | 0 |
| Asthma | 28 | 2.0263588 | 0 |
| Allograft rejection | 35 | 2.0152922 | 0 |
| Chemokine signaling pathway | 187 | 1.9651372 | 0 |
| Autoimmune thyroid disease | 50 | 1.9487576 | 0 |
| Primary immunodeficiency | 35 | 1.8914527 | 0.004106776 |
| Toll like receptor signaling pathway | 102 | 1.7821109 | 0.006198347 |
| Complement and coagulation cascades | 69 | 1.7474798 | 0.005870841 |
| ECM receptor interaction | 84 | 1.7368731 | 0.026748972 |
| ABC transporters | 44 | 1.7211503 | 0.00984252 |
| JAK stat signaling pathway | 155 | 1.7083862 | 0.012244898 |
| Fc gamma r mediated phagocytosis | 95 | 1.6750118 | 0.015841585 |
| B cell receptor signaling pathway | 75 | 1.6455605 | 0.032 |
| Hypertrophic cardiomyopathy hcm | 83 | 1.6157279 | 0.030991735 |
| Glycosaminoglycan degradation | 21 | 1.5970777 | 0.045908183 |
| Renin angiotensin system | 17 | 1.5964756 | 0.029880479 |
| Neuroactive ligand receptor interaction | 271 | 1.5705323 | 0.010309278 |
| Fc epsilon ri signaling pathway | 79 | 1.550188 | 0.01953125 |
| Regulation of actin cytoskeleton | 212 | 1.5455457 | 0.022727273 |
| Notch signaling pathway | 47 | 1.4989547 | 0.04496788 |
Note: GSEA, Gene Set Enrichment Analysis.
APOC1 was associated with ESCA immune cell infiltration
In the TIMER database, Pearson correlation analysis revealed a significant correlation of APOC1 expression with the tumor purity, B cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells (DCs) (Figure S3). Among ESCA tissues from TCGA database, APOC1 expression positively correlated with the ESTIMATE, immune, and stromal scores in ESCA using Pearson correlation analysis (Figure 9A-C). Furthermore, APOC1 expression significantly correlated with B cells, eosinophils, pDC, T helper cells, NK cells, Tem, TFH, DC, NK CD56dim cells, CD8+ T cells, aDC, Th1 cells, T cells, TReg, cytotoxic cells, iDC, macrophages, and others in ESCA (Figures 9D-P and S4). The ESTIMATE, immune, and stromal scores were abnormal in the high- and low-APOC1 expression groups as grouped by the median value of APOC1 expression (Figure S5). Further, T cells, T helper cells, Tem, TReg, TFH, pDC, Th1, aDC, B cells, NK cells, CD8+ T cells, cytotoxic cells, DCs, eosinophils, and other immune cells were abnormally expressed in the high- and low-APOC1 expression groups (Figure 10).
Figure 9.
Correlation of APOC1 expression with the immune cells in ESCA. A. Stromal score; B. Immune score; C. ESTIMATE score; D. Macrophages; E. iDC; F. Cytotoxic cells; G. TReg; H. T cells; I. Th1 cells; J. aDC; K. CD8 T cells; L. NK CD56dim cells; M. DC; N. TFH; O. Tem; P. NK cells. ESCA, Esophageal Cancer.
Figure 10.
Correlation of APOC1 expression with the immune infiltration in ESCA. A. T cells; B. T helper cells; C. Tcm; D. Tem; E. TReg; F. TFH; G. Tgd; H. Th17 cells; I. pDC; J. Th1 cells; K. Th2 cells; L. aDC; M. B cells; N. NK cells; O. CD8 T cells; P. Cytotoxic cells; Q. DC; R. Eosinophils; S. iDC; T. Macrophages; U. Mast cells; V. NK CD56bright cell; W. Neutrophils; X. NK CD56dim cells. ESCA, Esophageal Cancer; *, P < 0.05; **, P < 0.01; ***, P < 0.001; ns, Not statistically significant.
APOC1 overexpression is correlated with the ESCA immune infiltrating cell markers
In the correlation analysis module of the TIMER database, expression of APOC1 was associated with the levels of CD8A, IFNG, CD3D, FOXP3, CD3E, CD2, HLA-DRA, CD19, CD163, CD8B, VSIG4, STAT5A, MS4A4A, ITGAM, HLA-DPB1, PDCD1, HLA-DQB1, CD1C, ITGAX, NRP1, TBX21, STAT1, GATA3, CCR7, CCR8, STAT5B, STAT4, CTLA4, LAG3, HLA-DPA1, HAVCR2, and GZMB, regardless of the tumor purity (Figure 11 and Table 7). The relationship between most of the immune cell markers and APOC1 expression was confirmed using the GEPIA database (Figures 12 and S6).
Figure 11.
Association of APOC1 with the levels of immune cell markers under the conditions of non-tumor purity in TIMER database. A. CD8A; B. CD8B; C. CD3D; D. CD3E; E. CD2; F. CD79A; G. CD19; H. CD163; I. VSIG4; J. MS4A4A; K. CTLA4; L. PDCD1; M. HAVCR2; N. GZMB; O. LAG3.
Table 7.
Association of APOC1 expression with the immune cell marker expressions under the conditions of non-tumor or tumor purity
| Cell markers | Non-tumor purity | Tumor purity | ||
|---|---|---|---|---|
|
|
|
|||
| Cor | P | Cor | P | |
| CD8A | 0.46667867 | 2.66E-11 | 0.419671663 | 4.51E-09 |
| CD8B | 0.407329331 | 8.70E-09 | 0.364980379 | 4.72E-07 |
| CD3D | 0.448152079 | 2.33E-10 | 0.387981979 | 7.39E-08 |
| CD3E | 0.464554035 | 3.51E-11 | 0.405422768 | 1.64E-08 |
| CD2 | 0.457573633 | 8.19E-11 | 0.400739732 | 2.48E-08 |
| CD19 | 0.245201454 | 0.000768217 | 0.164961092 | 0.026901111 |
| CD79A | 0.200742959 | 0.006224018 | 0.121163445 | 0.105183281 |
| NOS2 | -0.136789735 | 0.063402652 | -0.121679666 | 0.103698276 |
| IRF5 | 0.154035101 | 0.036401056 | 0.111076524 | 0.137686046 |
| PTGS2 | -0.012450248 | 0.86630636 | -0.040969374 | 0.585023433 |
| CD163 | 0.611417308 | 0 | 0.578344015 | 1.86E-17 |
| VSIG4 | 0.658862439 | 0 | 0.629127277 | 3.15E-21 |
| MS4A4A | 0.755558925 | 0 | 0.735734442 | 6.07E-32 |
| CEACAM8 | -0.043029998 | 0.560853773 | -0.068040549 | 0.36411476 |
| ITGAM | 0.545017247 | 0 | 0.503922599 | 5.55E-13 |
| CCR7 | 0.227589932 | 0.001879205 | 0.149260216 | 0.045521706 |
| HLA-DPB1 | 0.526187408 | 0 | 0.480887822 | 8.34E-12 |
| HLA-DQB1 | 0.328050491 | 5.83E-06 | 0.273737912 | 0.000200593 |
| HLA-DRA | 0.463475607 | 4.02E-11 | 0.419042704 | 4.78E-09 |
| HLA-DPA1 | 0.489373034 | 0 | 0.44607355 | 3.48E-10 |
| CD1C | 0.36399182 | 3.52E-07 | 0.293353256 | 6.43E-05 |
| NRP1 | 0.433423676 | 1.05E-09 | 0.394195146 | 4.37E-08 |
| ITGAX | 0.679964747 | 0 | 0.659768538 | 7.34E-24 |
| TBX21 | 0.479165837 | 5.21E-12 | 0.423403412 | 3.18E-09 |
| STAT4 | 0.491499564 | 0 | 0.437745899 | 8.00E-10 |
| STAT1 | 0.310558735 | 1.86E-05 | 0.266484101 | 0.000299236 |
| IFNG | 0.426015304 | 1.49E-09 | 0.373271249 | 2.46E-07 |
| TNF | 0.06080323 | 0.410632226 | 0.002225182 | 0.976349379 |
| GATA3 | 0.333340283 | 4.04E-06 | 0.284327919 | 0.000109629 |
| STAT6 | -0.029121337 | 0.693694062 | -0.005441508 | 0.942206058 |
| STAT5A | 0.197742694 | 0.007055398 | 0.165760518 | 0.026160254 |
| IL13 | 0.179041415 | 0.014750095 | 0.126007961 | 0.091888006 |
| BCL6 | 0.125580911 | 0.088515862 | 0.115121387 | 0.12383454 |
| IL21 | 0.160170311 | 0.029415997 | 0.111592651 | 0.13585537 |
| STAT3 | -0.07916872 | 0.283797218 | -0.110320996 | 0.140399792 |
| IL17A | -0.08746033 | 0.236493776 | -0.085289793 | 0.254959189 |
| FOXP3 | 0.507260907 | 0 | 0.45905102 | 9.10E-11 |
| CCR8 | 0.47297766 | 1.06E-11 | 0.428596632 | 1.94E-09 |
| STAT5B | 0.161839961 | 0.02784297 | 0.170284702 | 0.02228878 |
| TGFB1 | 0.193707593 | 0.008329308 | 0.131337886 | 0.078844978 |
| PDCD1 | 0.404876616 | 1.48E-08 | 0.34857135 | 1.62E-06 |
| CTLA4 | 0.483819795 | 7.32E-13 | 0.431513348 | 1.47E-09 |
| LAG3 | 0.490447671 | 0 | 0.442887002 | 4.80E-10 |
| HAVCR2 | 0.75387021 | 0 | 0.733515964 | 1.14E-31 |
| GZMB | 0.438501194 | 6.33E-10 | 0.385415792 | 9.16E-08 |
Figure 12.
Association of APOC1 with the expression of immune cell markers in GEPIA database. A. CD8A; B. CD8B; C. CD3D; D. CD3E; E. CD2; F. CD19; G. CD163; H. VSIG4; I. MS4A4A; J. ITGAM; K. CCR7; L. HLA-DPB1; M. HLA-DQB1; N. HLA-DRA; O. HLA-DPA1; P. CD1C; Q. NRP1; R. ITGAX; S. TBX21; T. STAT4; U. STAT1; V. IFNG; W. GATA3; X. STAT5A.
Discussion
ESCA is a highly incident malignant tumor that severely affects the long-term quality of life of cancer patients. Therefore, it is crucial to develop new treatment modalities to improve the quality of life and long-term prognosis of ESCA patients. Studies have found that APOC1, a member of the apolipoprotein family, is critical for the progression of several cancers. Thus, inhibiting the expression of APOC1 is expected to delay cancer progression and improve the prognosis of cancer in patients, thereby serving as a potential target for cancer therapy [10-14,24-27]. For example, APOC1 overexpression in breast cancer tissues is associated with the late TNM stage and lymph node metastasis. Thus, APOC1 enhances the proliferation, invasion, and migration of MDA-MB-231 and MCF-7 breast cancer cells in vitro [24]. Likewise, as discussed earlier, APOC1 is highly expressed in CRC tissues, and its downregulation inhibits CRC cell growth and migration, induces cell cycle arrest, and increases apoptosis [10,13]. Ren et al. preliminarily reported that AOPC1 was overexpressed in ESCA tissues. Overexpression of APOC1 was related to the OS of ESCA patients, and not verified by clinical tissue and cells [27]. Similarly, APOC1 was overexpressed in unpaired and paired ESCA tissues in our study using bioinformatics analysis and clinical tissues, and its overexpression was not conducive to the OS, DSS, and PFI of patients with ESCA. In addition, we found that APOC1 overexpression was also significantly correlated with the T stage, pathological stage, histological grade, and BMI in ESCA patients. These observations suggest that APOC1 could be used as a promising biomarker for the prognosis and diagnosis of ESCA.
Studies have shown that APOC1 is involved in cancer progression via EMT, MAPK/JNK, STAT3, WNT3A, and other signaling pathways [10-14,24-27]. Specifically, APOC1 reduces E-cadherin expression and promotes vimentin expression in MDA-MB-231 and MCF-7 breast cancer cell lines. APOC1 is also involved in breast cancer progression through the regulation of the JNK/MAPK signaling mechanism [24]. Downregulation of APOC1 expression reduces the protein expression of N-cadherin, vimentin, Twist, Slug, Snail, and CD44 in cervical cancer cells and increases the protein expression of E-cadherin, facilitating its participation in the EMT process [12]. Furthermore, APOC1 overexpression is positively correlated with the progression of ccRCC. The EMT mediated metastasis of ccRCC is promoted by APOC1, whereas its downregulation inhibits EMT. Moreover, APOC1, a novel pro-transfer factor, activates STAT3 to enhance the metastasis of ccRCC cells [14]. In our study, APOC1 overexpression was involved in the T cell receptor, chemokine, Toll-like receptor, B cell receptor, JAK-STAT, and NOTCH signaling pathways and the pathways involving ABC transporters. Furthermore, STAT3 is one of the hub members of the JAK-STAT signaling pathway, which indicates that APOC1 might be involved in the JAK-STAT pathway to regulate ESCA progression. The T cell receptor, chemokine, Toll-like receptor, ABC transporters, B cell receptor, and NOTCH signaling pathways play crucial roles in cancer progression [28-33]. In our cell models, we confirmed for the first time that inhibition of APOC1 expression significantly reduced proliferation, migration, and invasion of ESCA cells. However, the roles of APOC1 in these mechanisms need to be further validated by western blotting of ESCA cells.
The tumor immune microenvironment plays a pivotal role in cancer progression [34-37], and the immune microenvironment of ESCA is no exception [38-40]. For example, L1CAM expression was significantly elevated in ESCC tissues and correlated with poor prognosis in patients with ESCC. Downregulation of L1CAM expression in ESCC cells inhibits tumor growth and migration and increases tumor cell apoptosis. In the tumor microenvironment, L1CAM expression affects CCL22 secretion and is correlated with Treg cell infiltration in ESCC. L1CAM promotes CCL22 expression and Treg cell recruitment by activating the PI3K/AKT/NF-κB signaling pathway, which, in turn, secretes TGF-β to positively regulate L1CAM expression [39]. Increased CCL2 expression correlates with tumor-associated macrophage accumulation during the ESCA development. Studies have shown that blocking the CCL2-CCR2 signaling axis can reduce tumor incidence by impeding tumor-associated macrophage recruitment in vivo, thereby enhancing the antitumor efficacy of CD8+ T cells in the tumor microenvironment. M2 cell polarization increases PD-L2 expression in tumor-associated macrophages, leading to immune evasion and tumor promotion through the PD-1 signaling pathway [40]. In this study, we found that APOC1 was associated with tumor purity, B cells, CD4+ T cells, macrophages, neutrophils, DCs, T cells, T helper cells, Tem, TReg, TFH, pDC, Th1, aDC, B cells, NK cells, CD8+ T cells, cytotoxic cells, and eosinophil levels in ESCA. This result corroborates previous studies showing an association of APOC1 with the ESCA immune microenvironment. In the TIMER and GEPIA databases, we found that APOC1 expression correlated with the expression of immune cell markers CD8A, IFNG, CD3D, FOXP3, CD3E, CD2, HLA-DRA, CD19, CD163, CD8B, VSIG4, STAT5A, MS4A4A, ITGAM, HLA-DPB1, PDCD1, HLA-DQB1, CD1C, ITGAX, NRP1, TBX21, STAT1, GATA3, CCR7, CCR8, STAT5B, STAT4, CTLA4, LAG3, HLA-DPA1, HAVCR2, and GZMB. These markers are closely associated with cancer progression [22,41-44]. For instance, circUHRF1 is associated with poor prognosis and NK cell dysfunction in patients with hepatocellular carcinoma (HCC). The secretion of IFN-γ and TNF-α derived from NK cells is inhibited by circUHRF1 which upregulates the expression of TIM-3 by degrading the expression of miR-449c-5p. As a result, the function of NK cells inhibited. Resistance against the anti-target PD1 therapy in patients with HCC may be caused by circUHRF1 [41]. However, the relationship between APOC1 and ESCA immune cell markers in the immune microenvironment needs to be further investigated.
In conclusion, APOC1 mRNA and protein expression were significantly elevated in ESCA tissues, as analyzed using the TCGA database and clinical tissues. Downregulation of APOC1 significantly inhibited the ESCA progression. However, the limitations of our study included lack of experimental validation in vivo, and more tissue samples and patient information to evaluate the values of APOC1 in patients with ESCA, which we plan to explore in future studies. The expression of APOC1 might be critical for the diagnosis of ESCA, since it was associated with the T stage, pathological stage, BMI, histological grade, and dismal prognosis of ESCA patients. Additionally, APOC1 was also associated with ESCA immune cell infiltration and thus might be involved in the ESCA progression by participating in cytokine-cytokine receptor interactions, T cell receptor, chemokine, B cell receptor, and other immune mechanisms.
Acknowledgements
This research was funded and supported by the Health Commission of Hubei Province scientific research project (no. WJ2019Q014) and Hubei Chen Xiaoping science and Technology Development Fund (no. CXPJJH1200002-2020040).
Disclosure of conflict of interest
None.
Abbreviations
- APOC1
Apolipoprotein C1
- EMT
Epithelial To Mesenchymal Transition
- NK
Natural Killer
- CRC
Colorectal Cancer
- ccRCC
Renal Cell Carcinoma
- DCs
Dendritic Cells
- ESCA
Esophageal Cancer
- ESCC
Esophageal Squamous Cell Carcinoma
- ROC
Receiver Operating Characteristic
- GSEA
Gene Set Enrichment Analysis
- KEGG
Kyoto Encyclopedia of Genes and Genomes
- OS
Overall Survival
- TCGA
The Cancer Genome Atlas
Figures S1-S6
Table S1
References
- 1.Guo Q, Peng Y, Yang H, Guo J. Prognostic nomogram for postoperative patients with gastroesophageal junction cancer of no distant metastasis. Front Oncol. 2021;11:643261. doi: 10.3389/fonc.2021.643261. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Fan J, Liu Z, Mao X, Tong X, Zhang T, Suo C, Chen X. Global trends in the incidence and mortality of esophageal cancer from 1990 to 2017. Cancer Med. 2020;9:6875–6887. doi: 10.1002/cam4.3338. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.McGrath AC, Sandhu G, Walpole E, McCaffrey E, Hollingworth SA. Survival outcomes in patients with non-small-cell lung cancer treated with erlotinib. Anticancer Drugs. 2018;29:786–790. doi: 10.1097/CAD.0000000000000640. [DOI] [PubMed] [Google Scholar]
- 4.Wang Z, Zhang B, Zhang C, Ren S, Wang W, Wang Y, Jiang T, Wei H, Li Y, Wu Q, Chen J. Effect of region on the outcome of patients receiving PD-1/PD-L1 inhibitors for advanced cancer. Int Immunopharmacol. 2019;74:105709. doi: 10.1016/j.intimp.2019.105709. [DOI] [PubMed] [Google Scholar]
- 5.Upadhya A, Yadav KS, Misra A. Targeted drug therapy in non-small cell lung cancer: clinical significance and possible solutions-part I. Expert Opin Drug Deliv. 2021;18:73–102. doi: 10.1080/17425247.2021.1825377. [DOI] [PubMed] [Google Scholar]
- 6.Zhao Y, Wang H, He C. Drug resistance of targeted therapy for advanced non-small cell lung cancer harbored EGFR mutation: from mechanism analysis to clinical strategy. J Cancer Res Clin Oncol. 2021;147:3653–3664. doi: 10.1007/s00432-021-03828-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Li XF, Aierken AL, Shen L. IPO5 promotes malignant progression of esophageal cancer through activating MMP7. Eur Rev Med Pharmacol Sci. 2020;24:4246–4254. doi: 10.26355/eurrev_202004_21004. [DOI] [PubMed] [Google Scholar]
- 8.Qin G, Lian J, Yue D, Chen X, Nan S, Qi Y, Li B, Cui G, Li X, Zhao S, Zhang Y. Musashi1, a potential prognostic marker in esophageal squamous cell carcinoma. Oncol Rep. 2017;38:1724–1732. doi: 10.3892/or.2017.5809. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Li Y, Jiang X, Yan X, Wang Y. Upregulation of SIX4 indicates poor clinical outcome and promotes tumor growth and cell metastasis in esophageal squamous cell carcinoma. Thorac Cancer. 2021;12:752–759. doi: 10.1111/1759-7714.13832. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Ren H, Chen Z, Yang L, Xiong W, Yang H, Xu K, Zhai E, Ding L, He Y, Song X. Apolipoprotein C1 (APOC1) promotes tumor progression via MAPK signaling pathways in colorectal cancer. Cancer Manag Res. 2019;11:4917–4930. doi: 10.2147/CMAR.S192529. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Yi J, Ren L, Wu J, Li W, Zheng X, Du G, Wang J. Apolipoprotein C1 (APOC1) as a novel diagnostic and prognostic biomarker for gastric cancer. Ann Transl Med. 2019;7:380. doi: 10.21037/atm.2019.07.59. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Shi X, Wang J, Dai S, Qin L, Zhou J, Chen Y. Apolipoprotein C1 (APOC1): a novel diagnostic and prognostic biomarker for cervical cancer. Onco Targets Ther. 2020;13:12881–12891. doi: 10.2147/OTT.S280690. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Xiao H, Xu Y. Overexpression of apolipoprotein C1 (APOC1) in clear cell renal cell carcinoma and its prognostic significance. Med Sci Monit. 2021;27:e929347. doi: 10.12659/MSM.929347. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Li YL, Wu LW, Zeng LH, Zhang ZY, Wang W, Zhang C, Lin NM. ApoC1 promotes the metastasis of clear cell renal cell carcinoma via activation of STAT3. Oncogene. 2020;39:6203–6217. doi: 10.1038/s41388-020-01428-3. [DOI] [PubMed] [Google Scholar]
- 15.Cheng L, Wang Q, Tao X, Qin Y, Wu Q, Zheng D, Chai D, Zhang Y, Lu D, Ci H, Wang Z, Ma J, Wang D, Cheng Z, Wu S, Tao Y. FOXM1 induces Vasculogenic mimicry in esophageal cancer through β-catenin/Tcf4 signaling. Diagn Pathol. 2020;15:14. doi: 10.1186/s13000-020-00929-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Chen C, Guo Q, Tang Y, Qu W, Zuo J, Ke X, Song Y. Screening and evaluation of the role of immune genes of brain metastasis in lung adenocarcinoma progression based on the TCGA and GEO databases. J Thorac Dis. 2021;13:5016–5034. doi: 10.21037/jtd-21-935. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Lubowicka E, Zbucka-Kretowska M, Sidorkiewicz I, Zajkowska M, Gacuta E, Puchnarewicz A, Chrostek L, Szmitkowski M, Ławicki S. Diagnostic power of cytokine M-CSF, metalloproteinase 2 (MMP-2) and tissue inhibitor-2 (TIMP-2) in cervical cancer patients based on ROC analysis. Pathol Oncol Res. 2020;26:791–800. doi: 10.1007/s12253-019-00626-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Li D, Liu SH, Liu QY, Zou QQ, Lv L, Liu GL, Wu Y. Analysis of the role and regulatory mechanism of hsa-miR-504 in cervical cancer based on the cancer genome atlas database. Cancer Biother Radiopharm. 2021;36:511–520. doi: 10.1089/cbr.2020.3798. [DOI] [PubMed] [Google Scholar]
- 19.Tu H, Wu M, Huang W, Wang L. Screening of potential biomarkers and their predictive value in early stage non-small cell lung cancer: a bioinformatics analysis. Transl Lung Cancer Res. 2019;8:797–807. doi: 10.21037/tlcr.2019.10.13. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Gan Y, Zheng S, Baak JP, Zhao S, Zheng Y, Luo N, Liao W, Fu C. Prediction of the anti-inflammatory mechanisms of curcumin by module-based protein interaction network analysis. Acta Pharm Sin B. 2015;5:590–5. doi: 10.1016/j.apsb.2015.09.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Liu D, Zhou Z, Guo Y, Du Q, Li L. CircCDK1 knockdown reduces CDK1 expression by targeting miR-489-3p to suppress the development of breast cancer and strengthen the sensitivity of Tamoxifen. Anticancer Drugs. 2022;33:286–299. doi: 10.1097/CAD.0000000000001266. [DOI] [PubMed] [Google Scholar]
- 22.Guo Q, Wang SH, Ji YM, Tong S, Li D, Ding XC, Wu CY. The roles and mechanisms of TRAT1 in the progression of non-small cell lung cancer. Curr Med Sci. 2022 doi: 10.1007/s11596-022-2625-1. [Epub ahead of print] [DOI] [PubMed] [Google Scholar]
- 23.Li T, Fan J, Wang B, Traugh N, Chen Q, Liu JS, Li B, Liu XS. TIMER: a web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res. 2017;77:e108–e110. doi: 10.1158/0008-5472.CAN-17-0307. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Zhang H, Wang Y, Liu C, Li W, Zhou F, Wang X, Zheng J. The apolipoprotein C1 is involved in breast cancer progression via EMT and MAPK/JNK pathway. Pathol Res Pract. 2022;229:153746. doi: 10.1016/j.prp.2021.153746. [DOI] [PubMed] [Google Scholar]
- 25.Jiang H, Tang JY, Xue D, Chen YM, Wu TC, Zhuang QF, He XZ. Apolipoprotein C1 stimulates the malignant process of renal cell carcinoma via the Wnt3a signaling. Cancer Cell Int. 2021;21:41. doi: 10.1186/s12935-020-01713-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Wang HJ, Ma YX, Wang AH, Jiang YS, Jiang XZ. Expression of apolipoprotein C1 in clear cell renal cell carcinoma: an oncogenic gene and a prognostic marker. Kaohsiung J Med Sci. 2021;37:419–426. doi: 10.1002/kjm2.12328. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Ren L, Yi J, Yang Y, Li W, Zheng X, Liu J, Li S, Yang H, Zhang Y, Ge B, Zhang S, Fu W, Dong D, Du G, Wang X, Wang J. Systematic pan-cancer analysis identifies APOC1 as an immunological biomarker which regulates macrophage polarization and promotes tumor metastasis. Pharmacol Res. 2022;183:106376. doi: 10.1016/j.phrs.2022.106376. [DOI] [PubMed] [Google Scholar]
- 28.Estrada A 3rd, Rodriguez AC, Rodriguez G, Grant AH, Ayala-Marin YM, Arrieta AJ, Kirken RA. Phosphorylation of CrkL S114 induced by common gamma chain cytokines and T-cell receptor signal transduction. Sci Rep. 2021;11:16951. doi: 10.1038/s41598-021-96428-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Yi L, Zhou X, Li T, Liu P, Hai L, Tong L, Ma H, Tao Z, Xie Y, Zhang C, Yu S, Yang X. Notch1 signaling pathway promotes invasion, self-renewal and growth of glioma initiating cells via modulating chemokine system CXCL12/CXCR4. J Exp Clin Cancer Res. 2019;38:339. doi: 10.1186/s13046-019-1319-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Gergen AK, Kohtz PD, Halpern AL, White AM, Meng X, Fullerton DA, Weyant MJ. Statins inhibit Toll-like receptor 4-mediated growth of human esophageal adenocarcinoma cells. J Surg Res. 2021;260:436–447. doi: 10.1016/j.jss.2020.11.016. [DOI] [PubMed] [Google Scholar]
- 31.Vrana D, Hlavac V, Brynychova V, Vaclavikova R, Neoral C, Vrba J, Aujesky R, Matzenauer M, Melichar B, Soucek P. ABC transporters and their role in the neoadjuvant treatment of esophageal cancer. Int J Mol Sci. 2018;19:868. doi: 10.3390/ijms19030868. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.von Wenserski L, Schultheiß C, Bolz S, Schliffke S, Simnica D, Willscher E, Gerull H, Wolters-Eisfeld G, Riecken K, Fehse B, Altfeld M, Nollau P, Binder M. SLAMF receptors negatively regulate B cell receptor signaling in chronic lymphocytic leukemia via recruitment of prohibitin-2. Leukemia. 2021;35:1073–1086. doi: 10.1038/s41375-020-01025-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Han H, Yang C, Zhang S, Cheng M, Guo S, Zhu Y, Ma J, Liang Y, Wang L, Zheng S, Wang Z, Chen D, Jiang YZ, Lin S. METTL3-mediated m6A mRNA modification promotes esophageal cancer initiation and progression via Notch signaling pathway. Mol Ther Nucleic Acids. 2021;26:333–346. doi: 10.1016/j.omtn.2021.07.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Luo X, Xu J, Yu J, Yi P. Shaping immune responses in the tumor microenvironment of ovarian cancer. Front Immunol. 2021;12:692360. doi: 10.3389/fimmu.2021.692360. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Lee SJ, Yang H, Kim WR, Lee YS, Lee WS, Kong SJ, Lee HJ, Kim JH, Cheon J, Kang B, Chon HJ, Kim C. STING activation normalizes the intraperitoneal vascular-immune microenvironment and suppresses peritoneal carcinomatosis of colon cancer. J Immunother Cancer. 2021;9:e002195. doi: 10.1136/jitc-2020-002195. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Öjlert ÅK, Halvorsen AR, Nebdal D, Lund-Iversen M, Solberg S, Brustugun OT, Lingjaerde OC, Helland Å. The immune microenvironment in non-small cell lung cancer is predictive of prognosis after surgery. Mol Oncol. 2019;13:1166–1179. doi: 10.1002/1878-0261.12475. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Philippou Y, Sjoberg HT, Murphy E, Alyacoubi S, Jones KI, Gordon-Weeks AN, Phyu S, Parkes EE, Gillies McKenna W, Lamb AD, Gileadi U, Cerundolo V, Scheiblin DA, Lockett SJ, Wink DA, Mills IG, Hamdy FC, Muschel RJ, Bryant RJ. Impacts of combining anti-PD-L1 immunotherapy and radiotherapy on the tumour immune microenvironment in a murine prostate cancer model. Br J Cancer. 2020;123:1089–1100. doi: 10.1038/s41416-020-0956-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Davern M, Donlon NE, Power R, Hayes C, King R, Dunne MR, Reynolds JV. The tumour immune microenvironment in oesophageal cancer. Br J Cancer. 2021;125:479–494. doi: 10.1038/s41416-021-01331-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Zhao X, Liu S, Chen X, Zhao J, Li F, Zhao Q, Xie T, Huang L, Zhang Z, Qi Y, Yang Y, Zhao S, Zhang Y. L1CAM overexpression promotes tumor progression through recruitment of regulatory T cells in esophageal carcinoma. Cancer Biol Med. 2021;18:547–61. doi: 10.20892/j.issn.2095-3941.2020.0182. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Yang H, Zhang Q, Xu M, Wang L, Chen X, Feng Y, Li Y, Zhang X, Cui W, Jia X. CCL2-CCR2 axis recruits tumor associated macrophages to induce immune evasion through PD-1 signaling in esophageal carcinogenesis. Mol Cancer. 2020;19:41. doi: 10.1186/s12943-020-01165-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Zhang PF, Gao C, Huang XY, Lu JC, Guo XJ, Shi GM, Cai JB, Ke AW. Cancer cell-derived exosomal circUHRF1 induces natural killer cell exhaustion and may cause resistance to anti-PD1 therapy in hepatocellular carcinoma. Mol Cancer. 2020;19:110. doi: 10.1186/s12943-020-01222-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Li Q, Gao JF, Qi BL. PDCD1 strengthens the sensitivity of ovarian cancer to cisplatin chemotherapy by promoting apoptosis. J BUON. 2017;22:746–756. [PubMed] [Google Scholar]
- 43.Huang J, Chen P, Liu K, Liu J, Zhou B, Wu R, Peng Q, Liu ZX, Li C, Kroemer G, Lotze M, Zeh H, Kang R, Tang D. CDK1/2/5 inhibition overcomes IFNG-mediated adaptive immune resistance in pancreatic cancer. Gut. 2021;70:890–899. doi: 10.1136/gutjnl-2019-320441. [DOI] [PubMed] [Google Scholar]
- 44.Hall BM, Gleiberman AS, Strom E, Krasnov PA, Frescas D, Vujcic S, Leontieva OV, Antoch MP, Kogan V, Koman IE, Zhu Y, Tchkonia T, Kirkland JL, Chernova OB, Gudkov AV. Immune checkpoint protein VSIG4 as a biomarker of aging in murine adipose tissue. Aging Cell. 2020;19:e13219. doi: 10.1111/acel.13219. [DOI] [PMC free article] [PubMed] [Google Scholar]
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